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• Dr Joanna Zawadzka

Geographical information is increasingly prevalent in our daily life, affecting personal leisure activities as much as business services and the workplace. Geographical information represents a key theme in environmental management and it has been estimated that some 80% of the data used for environmental, business and policy-oriented decision-making is geographical in nature. Such spatial data requires a structured approach in its management if the maximum benefit is to be derived from analysis and dissemination. This module provides a solid introduction to the issues concerning the management of spatial information and the tools to do so, with a predominant focus on ESRI’s software solutions. 

Spatial data models and database structures – vectors, rasters, topology, ordered and indexed lists, hierarchical, network, relational, object oriented, hybrid, metadata.
Mapping fundamentals – geodesy, projections, cartography, abstraction of the real world into map form.
Analysis approaches – database manipulation, reclassification, overlay, spatial modelling.
Data specification and standards – use cases, interoperability, metadata.
Data visualisation.

On successful completion of this module you should be able to:

• Define the functional components of a GIS, projections, data and modelling processes for managing spatial data.
• Organise and integrate, using appropriate data structures, spatial and aspatial data within a GIS.
• Analyse, evaluate and prepare data within appropriate spatial databases structures for information dissemination.
• Examine the role of metadata and FAIR data principles for enabling interoperability between spatial data infrastructures.
• Perform and disseminate the results of analysis and data manipulation via maps, tables and other appropriate media.

• Dr Daniel Simms

Deriving digital elevation models and ortho imagery is an important application of remote sensing data for many areas of spatial work. This module introduces techniques for the extraction of topographic information from remotely sensed data using digital photogrammetry techniques. Image interpretation is also a vital skill required in many image based mapping projects. The concepts and techniques of image interpretation will be introduced and practised.

• Topographic maps and remote sensing images: map scale and content, image sources and interpretation methods, accuracy issues.
• Aerial imagery in the context of other remote sensing systems. Physics of light: principles of recording the image. Stereoscopy and parallax. Geometry: scale variation, relief displacement, tilts.
• Geometry of vertical aerial imagery: geometry, co-ordinate axes, scale, measurement.
• Digital photogrammetry. Digital elevation models. Structure from Motion.
• Aerial imagery mosaics and orthophotos.
• Interpretation: principles and factors.  Applied interpretation: geology, geomorphology, vegetation, soils, urban structures.  Flight planning.  API project management and implementation.
• Recent developments - UAV imagery, digital aerial cameras.

 

On successful completion of this module you should be able to:

• Examine the geometry and spectral properties of vertical aerial imagery,
• Examine the basic principles of digital photogrammetry,
• Interpret aerial imagery to analyse the physical and human environments,
• Extract elevation data from stereo imagery in a digital photogrammetric environment,
• Derive orthophotography from survey quality aerial imagery.

• Dr Daniel Simms

Writing code opens-up new approaches to creative problem solving and allows us to move beyond the limitations of any particular software. This module aims to develop your skills and confidence to write your own code in the Python programming language and access a broad ecosystem of packages and tools that comprise the foundations of Data Science.

Fundamentals of programming.
Python syntax, types, calculations, variables, strings, and object-oriented programming concepts.
Branching, iteration, and recursion.
Abstraction, functions, and classes. 
Files, packages, and imports.
Testing, debugging, and exceptions.
Version control.
Writing efficient code.
Python scientific packages for data analysis and visualisation.
Geospatial data.
Jupyter Notebooks and Colaboratory.
Statistics for Data Science.

• Dr Abdou Khouakhi

The appropriate application of remote sensing to the monitoring of earth resources requires an understanding of basic physics and imaging technology. This subject introduces the basic radiometric concepts and physical relations and then gives you the practical tools to extract physical measurements from information from digital image data stored in the cloud. 

Physical principles:

• Electromagnetic radiation: Radiometric units and terms, radiation laws, radiation sources; optical, thermal and microwave.
• Atmospheric interactions and correction.
• Surface interactions and interpretation of spectral response patterns.
• Plant, soil and water spectral properties.
• Image formation: Passive systems (detectors, opto-mechanical line scanners, waveband separation, linear and area arrays) and active systems (Lidar, RAR and SAR concepts).
• Spatial resolution and geometry.
• Orbits and platforms.
• Review of satellite and airborne systems.
• Data reception and distribution: Data suppliers, product levels, access, manipulation (download and in the cloud such as Google Earth Engine).

 

• Dr Daniel Simms

Extracting information from satellite, aerial and drone image data requires knowledge of a wide variety of image processing techniques. This module will give you the skills to select and apply appropriate image processing techniques for the extraction and analysis of information from image data for applications of remote sensing, for example in land use monitoring and surface change detection.

Radiometric, spectral and spatial image enhancement: Contrast stretching (linear, bilinear, Gaussian, histogram equalisation and manual), digital filtering in the spatial domain (low-pass, high-pass, high-boost, median and directional).

Band algebra: Derivation of soil and vegetation indices: ratios, normalised differences, PVI, SBI, tasselled cap concept.

Geometric correction: Map projections, selection of ground control points, transform equations, resampling methods (nearest neighbour, bilinear interpolation, cubic convolution).

Supervised and unsupervised image classification: Parametric and non-parametric techniques, clustering, segmentation, pixel and object-based approaches, machine learning, deep learning, and validation (accuracy assessment).

Post processing, processing chains, change detection, cloud computing and applications.

On successful completion of this module you should be able to:

Evaluate a wide range of image processing techniques and their underlying mathematical principles.

Select appropriate image processing sequences to achieve predetermined objectives.

Operate and manage an image processing system.

Integrate image processing techniques into applications of remote sensing.

• Dr Abdou Khouakhi

GIS analyses are based upon increasingly sophisticated methods, but the results are subject to both error and uncertainty. A range of advanced methods are introduced that will have potential use to students in their group and thesis projects and their future careers. Emphasis will be given to the role of GIS in modelling environmental systems and the programming tools available to develop applications.

• QGIS Analysis Techniques: Geopackage use, database manipulation, geographic data handling, spatial modelling,
• Spatial Analysis: Digital terrain modelling,
• Transportation Network Modelling: Analysing and optimising transportation networks,
• Python in ArcGIS Pro: Object-oriented principles in ArcGIS, use of Editor and Notebook environments,
• ArcPy Scripting: Writing Python scripts for automation,
• Python Script Tools: Creation and management of Script Tools in ArcGIS Pro.

On successful completion of this module you should be able to:

• Assess the quality of geographic data.
• Apply advanced spatial analyses.
• Analyse the requirements of a proposed application and synthesise an appropriate solution.
• Develop scripts to efficiently run complex/time consuming processes.

• Dr Toby Waine

This module covers the importance of environmental resource surveys that are required to obtain the data used in environmental information management.

• Introduction to geographical resource survey. Why, when, where and how? Understanding constraints.
• Introduction to R: A software environment for statistical computing and graphics and its use in manipulating and visualising survey data.
• Survey strategies for environmental resources: Census with thematic mapping, ground sampling, sampling with property mapping, integrated ground sampling and property mapping.
• Development of classification schemes: User requirements, data availability, class definitions.
• Sampling and rapid estimates for plant communities, water and soil quality – biomass, cover and species assessment, count plot methods, plotless sample technique, soil and water survey techniques.
• Assessment of existing data quality and use in survey design.
• Statistical design and analysis for environmental resource surveys: Area frames, point samples, bulk samples, area samples, sampling at global scales, multi-scale sampling.
• Quality assessment of environmental data: Accuracy measures, effect of bias, quality measures and statistics, error and uncertainty sources and measures.
• Introduction to interpolation methods, generating maps from point survey data.
• Integration data sources and types (data fusion) and statistical models with survey data (model data fusion) to increase survey cost effectiveness.
• Review of example surveys.

On successful completion of this module you should be able to:

• Determine the appropriate survey method to undertake an assessment of environmental resources.
• Evaluate existing information and models which complement the survey method.
• Design and conduct field surveys for data collection and verification.
• Select and carry out appropriate modelling and statistical analyses.
• Summarise and present results of a survey for users effectively.

• Professor Stephen Hallett

Geographical information is increasingly prevalent in our daily life, affecting personal leisure activities as much as the workplace. Geographical information represents a key theme in environmental management and, indeed, it has been estimated that some 80% of the data used for environmental, business and policy-oriented decision-making is geographical in nature. This poses challenges to its efficient and timely dissemination to stakeholders and potential end users. Today, the Internet offers a ubiquitous medium for real-time delivery of geographical information and location-based web services, across a range of computing platforms.

Such approaches offer extremely powerful means to reach out to a broad audience of users, particularly those who do not necessarily have the required skills to access or operate specialist GIS software packages. Web mapping therefore offers a means to ‘democratise’ geospatial data. However, with a wealth of technical approaches available to develop contemporary web data services and mapping solutions, there needs to be a correspondingly sound understanding of these choices and approaches by developers if the maximum benefit is to be derived from their analysis and dissemination online. This module therefore provides a solid introduction to the management and dissemination of web mapping and spatial data on the Internet.

• Appraise contemporary Internet technologies and their application relevant to geographical data dissemination.
• Apply HTML and CSS to develop web frameworks, drawing on templates as required, demonstrating the application of JavaScript, and the use of mapping Application Programming Interfaces (APIs).
• Evaluate emergent Internet GIS standards and data transfer strategies and formats (e.g. GML; XML; JSON/GeoJSON; WKT).
• Prepare an internet mapping website, and appraise the utility and use of proprietary versus open-source contemporary internet mapping technologies (e.g. ESRI ArcGIS Enterprise and ArcGIS Server, ESRI JavaScript API, Google Maps API, Geoserver, Open Layers API, Leaflet API, MapKit API etc).
• Evaluate and compare leading proprietary and open-source data server technologies (e.g. GeoServer, ESRI ArcGIS Server, ESRI ArcGIS Online, QGIS, Oracle, Postgres/PostGIS) and examine and debate a selection of real-world internet mapping projects, examples, and case studies.

On successful completion of this module you should be able to:

• Appraise contemporary Internet technologies and their application relevant to geographical data dissemination.
• Apply HTML and CSS to develop web frameworks, drawing on templates as required, demonstrating the application of JavaScript, and the use of mapping Application Programming Interfaces (APIs).
• Evaluate emergent Internet GIS standards and data transfer strategies and formats (e.g. GML; XML; JSON/GeoJSON; WKT).
• Prepare an internet mapping website, and appraise the utility and use of proprietary versus open-source contemporary internet mapping technologies (e.g. ESRI ArcGIS Enterprise and ArcGIS Server, ESRI JavaScript API, Google Maps API, Geoserver, Open Layers API, Leaflet API, MapKit API etc).
• Evaluate and compare leading proprietary and open-source data server technologies (e.g. GeoServer, ESRI ArcGIS Server, ESRI ArcGIS Online, QGIS, Oracle, Postgres/PostGIS) and examine and debate a selection of real-world internet mapping projects, examples, and case studies.